Bridging the theory-practice gap in controls education is a well-known challenge. In this paper, we discuss how one can bridge this gap using a flipped classroom. Based on the recent MOOC (Massive Open Online Course), Control of Mobile Robots, we flipped the classroom in a senior robotics and controls class at the Georgia Institute of Technology. The students participated in the MOOC and came to class prepared to solve controls problems on robots. Key to this experience was not only the delivery of theoretical concepts via the MOOC, but also a hardware/software platform that provided a learning environment where exploratory, practical tinkering was grounded in solid theory. This paper reports on the findings of the flipped classroom experiment, as well as discusses why this classroom format is ideal for controls courses.

No abstract

No abstract

In this paper, we develop a novel algorithm for spacecraft trajectory planning in an environment cluttered with many geometrically-fixed obstacles. The Spherical Expansion and Sequential Convex Programming (SE-SCP) algorithm first uses a spherical-expansionbased sampling algorithm to explore the workspace. Once a path is found from the start position to the goal position, the algorithm generates a locally optimal trajectory within the homotopy class using sequential convex programming. If the number of samples tends to infinity, then the SE-SCP trajectory converges to the globally optimal trajectory in the workspace. The SE-SCP algorithm is computationally efficient, therefore it can be used for real-time applications on resource-constrained systems. We also present results of numerical simulations and comparisons with existing algorithms.

In this thesis, I address the issue of human-swarm interactions by proposing a new set of affrodances that make a multi-robot system amenable to human control. An affordance, as defined by Gibson [11], is a relation between an object and a user, where the object explicitly allows the user to perform a particular action. The identified affordances when controlling a swarm, include stretching the swarm, molding it into a particular shape, splitting and merging sub-swarms, and mixing of different swarms. The contribution beyond the formulation of these affordances is the coupling of an image recognition framework identified by an effective deformable-medium control interface, and the accompanying algorithms needed to identify the appropriate inputs, and then turn those into decentralized control laws for the individual robots. As result, the developed human-swarm interaction methodology is applied to a team of mobile robots.